Inland waters are important sources of greenhouse gases (GHG). The accurate quantification of fluxes is fundamental to assessing their roles in the global GHG cycle. The flux chamber method is the most widely used technique for measuring GHG fluxes in inland waters. Based on whether external air is introduced to maintain stable CO2 concentrations inside the chamber during measurements, flux chambers can be categorized into two types: closed path (non-steady-state) chamber and opened path (steady-state) chamber. We reviewed the basic principles, practical applications, and respective advantages and limitations of those two types. The closed path chamber method is characterized by flexible deployment and simple operation, but it may disturb the micrometeorological conditions within the chamber. In contrast, the opened path chamber method maintains consistent environmental conditions between the chamber interior and the ambient atmosphere, but it imposes higher requirements on the precision of gas analyzers and the stability of control systems. Moreover, we discussed the uncertainties in flux measurements arising from factors such as chamber design (size and shape), observation duration, and gas transfer velocity. We further summarized key operational considerations, including chamber airtightness, pressure equili-brium, gas mixing conditions, and the measurement of gas mixing ratios. Finally, we outlined future directions and application prospects of the flux chamber method in inland water GHG research, aiming to provide a reference for method selection and technological advancement in this field.

Inland water bodies are important CH4 sources. The accurate observation of CH4 fluxes is key to quantitatively assessing emissions. The relaxed eddy accumulation (REA) method is a technique for calculating material fluxes using the differences of gas concentration in upward and downward air movements over a period, the standard deviation of vertical wind speed (σw), and an empirical coefficient b. We utilized observation data obtained with the eddy covariance (EC) method at the East Taihu Lake site (DTH site) of the mesoscale flux observation network in the large water body of Lake Taihu and the Guandu small water body aquaculture pond site (GD site) in Anhui. By leveraging the similarity between physical quantities in the REA method and adopting the "proxy variable method", three types of five methods, including synchronous b-value, fixed b-value (including mean, median, and fitted slope), and corrected b-value, were used to determine the empirical coefficient b in REA on the basis of determining the optimal proxy variable, and ultimately obtain CH4 flux. We assessed the applicability of the REA method for observing CH4 fluxes in large and small inland water bodies and clarified the optimal of the key coefficient b. The results showed that among the b-values for all flux results at both sites, the b-value for water vapor flux exhibited the smallest dispersion, making it suitable as the optimal proxy variable for the REA method. The interquartile range (the difference between the third and first quartiles) of the water vapor flux b-value showed a trend of first decreasing and then increasing with the increase of the vertical wind speed threshold (wd). In this study, wd was set to 0.4 times σw. Compared with CH4 fluxes observed by the EC method, at the DTH site, CH4 fluxes calculated using the fixed b-value mean and fixed b-value median performed best. At the GD site, CH4 fluxes calculated using the fixed b-value fitted slope performed best. The optimal b-values for the DTH and GD sites were 0.443 and 0.500, respectively. CH4 fluxes obtained by the REA method at both sites showed good consistency with those obtained by the EC method, indicating that the REA method is applicable for observing CH4 fluxes over inland water underlying surfaces. Generally, the b-value of large water bodies is smaller than that of small water bodies.

To characterize the concentrations of CO2 and CH4 in the urban atmospheric boundary layer (ABL), this study conducted airborne measurements over four cities in Eastern China, obtaining full vertical profiles (ground to 2 km) over Beijing and Nanjing, partial profiles over Hengshiu and Shangqiu. Results showed that the CO2 and CH4 concentrations in the ABL were consistently higher than those in the free atmosphere, with the highest values observed near the surface (Beijing and Nanjing). In Beijing, the daytime and nighttime inversion jumps in the CO2 concentration were -25.2 +/- 0.4 and -18.0 +/- 0.2 ppm, respectively. In Nanjing, the corresponding values were -9.5 +/- 0.1 and -10.0 +/- 0.4 ppm. For CH4, the inversion jumps were -171.4 +/- 0.4 ppb during the day and -202.6 +/- 2.3 ppb at night (Beijing); in Nanjing, they were -140.7 +/- 0.1 and -108.0 +/- 2.1 ppb, respectively. Change in the airmass trajectory altered the free-atmospheric CO2 concentration over Nanjing by 3 ppm in a matter of a few hours. The EDGAR CH4:CO2 emissions ratio was within measurement uncertainty of the nighttime ABL value in Beijing, but about 80 % higher in Nanjing, indicating that the inventory may have missed the recent energy transition from gasoline and natural gas to electric in the transport sector. The experimental data is available at 10.7910/DVN/ZPVSVU (Wang et al., 2026).

As an important component of inland waters, shallow lakes are hotspots for CO2 emissions. Due to the influence of eutrophication and aquatic macrophyte, CO2 fluxes at the water-air interface of shallow lakes exhibit complex variability, posing challenges for high-accuracy simulation. To compare the performance of different machine learning models in simulating CO2 fluxes in shallow lakes, we focused on a floating-leaved vegetation zone in eastern Lake Taihu. Based on CO2 flux observations from an eddy covariance system, combined with meteorological, water quality, and vegetation variables, we developed four machine learning models, random forest (RF), support vector machine (SVM), backpropagation neural network (BPNN), and long short-term memory network (LSTM). Then, we evaluated the performance under three modeling scenarios, including growing season, non-growing season, and whole-season. Among the three modeling scenarios, the whole-season modeling approach achieved the best overall performance, with test-set metrics consistently outperforming those of the seasonal models. The RF model exhibited the highest accuracy and robustness under all the three scenarios. In the whole-season mode-ling scenario, the RF model achieved a coefficient of determination (R2) of 0.72 and a root mean square error (RMSE) of 0.57 μmol·m-2·s-1. For the growing-season model, the RF performance yielded an R2 of 0.64 and an RMSE of 0.88 μmol·m-2·s-1, while in the non-growing-season model, the R2 and RMSE were 0.61 and 0.43 μmol·m-2·s-1, respectively. The SVM and BPNN models showed comparable but inferior performance, whereas the LSTM model performed relatively poorly. Furthermore, we used recursive feature elimination (RFE) to identify the optimal combination of driving factors for the RF model under the whole-season scenario. The selected feature set included: surface water temperature (Tw_20), sediment temperature (Ts), dissolved oxygen (DO), air tempera-ture (Ta), incoming shortwave radiation (Rs_in), wind speed (WS), total nitrogen (TN), water pH, friction velocity (u*), and normalized difference vegetation index (NDVI). This feature set further improved simulation accuracy (R2=0.76, RMSE=0.55 μmol·m-2·s-1) and effectively reduced model complexity. The SHAP analysis showed the significant influences of water temperature, radiation, dissolved oxygen, and vegetation index on CO2 fluxes. The results would provide a useful methodological reference for CO2 flux modeling and carbon cycle studies in shallow lakes.

Freshwater aquaculture systems are recognized as significant contributors to atmospheric methane (CH4) emissions, yet accurate quantification remains challenging due to high variability across different aquaculture types and the scarcity of high-frequency observations. To address these gaps, we conducted synchronous in-situ measurements of CH4 emissions from two typical aquaculture types - pond aquaculture and lake aquaculture - in the Yangtze River Delta, China, using the eddy covariance technique, capturing CH4 flux from hourly to annual scales. Our high-resolution measurements revealed a weak diurnal pattern in CH4 flux, although daily mean flux varied considerately at the two systems (lake aquaculture: 0.04 to 6.60 mu g CH4 m- 2 s- 1; pond aquaculture: 0.08 to 15.4 mu g CH4 m- 2 s-1). CH4 flux was significantly higher in the pond aquaculture compared to the lake aquaculture, with the annual mean value of 5.08 mu g CH4 m- 2 s- 1 (120 g CH4-C m- 2 yr- 1) and 1.52 mu g CH4 m- 2 s-1 (36 g CH4-C m- 2 yr- 1), respectively. Further analysis suggested that smaller size of the ponds, combined with higher nitrogen and carbon loadings and elevated chlorophyll-a concentrations, likely contributed to substantial emissions from the pond aquaculture. Ebullition was identified as the primary emission pathway, accounting for 70 % and 60 % of the total CH4 emissions from the pond and the lake system, respectively. While CH4 flux increased significantly with increasing temperature, the flux during the warming phase of the year was lower than during the cooling phase at equivalent temperatures. Additionally, CH4 flux in the pond system was less sensitive to temperature than in the lake system (Q10 for pond: 2.29; Q10 for lake: 5.75), likely due to the compound influence of biotic factors. Our findings underscore the importance of incorporating high-resolution emissions data from diverse aquaculture systems to accurately estimate the CH4 budget of freshwater aquaculture.

Lakes are crucial for the global carbon cycle. The impacts of aquaculture and "pen removal and lake ecological restoration" on lake carbon source and sink functions remain unclear. We continuously monitored CO2 fluxes in the aquaculture zones of East Lake Taihu during the aquaculture period (2018) and the ecological restoration period (2019-2020), to assess the effects of restoration on lake CO2 flux and its driving factors. The results showed that, regardless of the aquaculture or restoration stage, seasonal variations of CO2 fluxes followed a consistent pattern: net CO2 uptake during the growing season (May-October) and near-zero fluxes during the non-growing season (December-March). Diurnal CO2 flux patterns characterized by daytime uptake and nighttime release, which became more pronounced after restoration, with a significant increase in daytime uptake and a slight rise in nighttime emission. The reduction in external organic carbon input and the shift in dominant macrophyte communities from submerged plants to floating-leaf plants after restoration substantially enhanced net CO2 uptake of East Lake Taihu, with growing-season CO2 uptake increasing from 182.03 g CO2·m-2·a -1 in 2018 (aquaculture stage) to 384.17 and 629.19 g CO2·m-2·a -1 in 2019 and 2020, respectively. The diurnal CO2 flux dynamics were primarily driven by solar radiation. Both light-use efficiency and photosynthetic capacity of aquatic plants improved after restoration. At the daily scale, CO2 fluxes during the aquaculture period were regulated by temperature, solar radiation, and wind speed. After restoration, the effect of wind speed became insignificant, the temperature sensitivity (Q10) of daytime uptake increased from 2.44 in 2018 to 3.16 in 2019 and 3.03 in 2020; and the Q10 of nighttime emission declined from 10.20 in 2018 to 1.17 in 2019 and 5.14 in 2020. On the monthly scale, during the aquaculture phase, total nitrogen concentration was the primary controlling factor for lake CO2 flux, while the normalized difference vegetation index (NDVI) was the primary controlling factor for diurnal lake CO2 flux. After the cessation of aquaculture and the restoration of the lake, solar radiation and temperature became the primary controlling factors for lake CO2 flux, and the sensitivity of diurnal lake CO2 flux to changes in NDVI increased.

Open-path eddy covariance (OPEC) is widely used for measuring trace gas fluxes between the surface and the atmosphere. At a lake in Eastern China (Lake Taihu), the CO2 flux measured with OPEC was often negative at night (with values as low as - 22.1 mu mol m- 2 s- 1) and was coherent across the whole lake, as if the lake were a large sink of atmospheric CO2. The purpose of this study is to investigate the cause of this negative flux phenomenon. In addition to OPEC, we also used closed-path eddy covariance (CPEC) and the transfer coefficient (TC) method to measure the flux. The results show that the persistent negative CO2 flux phenomenon was observed with OPEC but not with CPEC or TC. Because air drawn into the CPEC analyzer was filtered but the OPEC analyzer was influenced by aerosol contamination, the most logical explanation was that particles deposited on the optical lens of the OPEC analyzer changed its cross-sensitivity to water vapor. The direct evidence of this interference was a strong positive correlation between the OPEC analyzer's signal strength and the CO2 mixing ratio observed at 10 Hz. We suggest that it is possible to perform post-field correction to this negative flux bias using the 10 Hz signal strength data. In comparison, an OPEC system at a nearby land site did not experience aerosol interference due to low water vapor flux at night and lack of hygroscopic growth of particles on the optical lens in low humidity conditions. The type of aerosol interference reported here may also occur in high humidity and high pollution conditions elsewhere.

Aquaculture ponds are inland small water bodies subject to human activities. They are not only carbon sources, but important sources of regional water evaporation. Accurate observation of carbon and water fluxes in aquaculture ponds is the basis for quantifying carbon emission and evaporation contribution of inland water. Nanjing University of Information Science and Technology set up an atmospheric environment experiment site to observe the carbon and water flux over aquaculture water bodies in Guandu Village of Quanjiao County, Anhui Province. This dataset includes the CO2 flux data (measured by the multi-channel closed dynamic floating chamber method), CH4 flux data (measured by the eddy covariance method, multi-channel closed dynamic floating chamber method, and inverted funnel method), latent heat and sensible heat fluxes data (measured by the eddy covariance method), as well as key environmental factors data, such as air temperature, water temperature, and radiation. All data were processed by standardized procedures. Moreover, data quality controls were performed. This dataset can provide important data for accurately estimating carbon emission and evaporation contribution in regional and global inland small water bodies.

湖泊“皮肤效应”指表面温度与表层水温的差异,量化“皮肤效应”并分析其影响因素有助于理解湖泊物理、化学、生物和生态过程对气候变暖的响应。本文基于太湖中尺度通量网2011—2020年水温梯度、辐射四分量和小气候观测数据,定量分析了在不同时间尺度和不同天气条件下“皮肤效应”的差异及其影响因素。结果表明,太湖暖“皮肤效应”在15:00—16:00最强,可达1.95℃;冷“皮肤效应”在7:00—8:00最强,达-0.50℃。“皮肤效应”强度春季最强,夏季最弱。因此,无法用表层水温观测值直接验证卫星午后过境反演得到的太湖湖面温度,其偏差可达2℃,尤其在春季。年际尺度上,太湖表面温度上升速率为0.14℃/a,与同期气温上升速率相当,表层水温上升速率为0.12℃/a。使用遥感反演的表面温度表征的太湖升温速率会比传统的表层水温观测结果快0.02℃/a。晴天小风时暖“皮肤效应”最强,为1.64℃;阴天大风时“皮肤效应”最弱,仅为0.32℃。相较于太阳辐射,风速对太湖水温“皮肤效应”的影响更大,风的扰动是影响太湖水温“皮肤效应”的首要因素。此外,基于10年观测数据建立了适用于太湖水温“皮肤效应”的风速参数化方案。

Although croplands are known tobe strong sources of anthropogenic N2O, large uncertainties still exist regarding their emission factors, that is, the proportion of N in fertilizer application that escapes to the atmosphere as N2O. In this study, we report the results of an experiment on the N2O flux in a landscape dominated by rice cultivation in the Yangtze River Delta, China. The observation was made with a closed-path eddy covariance system on a 70-m tall tower from October 2018 to December 2020 (27 months). Temperature and precipitation explained 78% of the seasonal and interannual variability in the observed N2O flux. The growing season (May to October) mean flux (1.14 nmol m-2 s-1) was much higher than the median flux found in the literature for rice paddies. The mean N2O flux during the observational period was 0.90 +/- 0.71 nmol m-2 s-1, and the annual cumulative N2O emission was 7.6 and 9.1 kg N2ON ha-1 during 2019 and 2020, respectively. The corresponding landscape emission factor was 3.8% and 4.6%, respectively, which were much higher than the IPCC default direct (0.3%) and indirect emission factors (0.75%) for rice paddies.

The chamber method is widely used to measure CO2 and CH4 flux in inland water. However, the designs of chamber used in various studies are different and lack unified standards, which would affect the observation results. To clarify the impacts of chamber characteristics, including light transmittance, air pressure difference inside and outside the chamber, and gas mixing degree in the chamber, on CO2 and CH4 flux measurements at the water-air interface, we compared the effects of transparent/opaque chamber, the chamber with/without air pressure equalizing device and fan on CO2 and CH4 flux measurements in the aquaculture pond, based on the multi-channel closed dynamic chamber system. The results showed that, during the daytime in summer, compared with the transparent chamber which could measure the actual CO2 flux, when CO2 was emitted from the pond, the opaque chamber overestimated the CO2 flux by 90%; when CO2 was absorbed by the pond, the opaque chamber underestimated the CO2 flux by 50%. The CH4 diffusion flux measured by the opaque chamber was 40% lower than that measured by the transparent chamber. There was no significant difference between CO2 and CH4 flux measured by the chamber with and without air pressure equalizing device. CO2 flux observed by the chamber without fan had poor representativeness, being 20% higher than that observed by the chamber with fan. Moreover, CH4 flux emitted through different pathways could not be distinguished using the chamber without fan. Therefore, when the chamber method was used to observe the CO2 and CH4 flux at the water-air interface, the chamber shall be transparent and be installed with fan.

Enclosure lake aquaculture causes lake eutrophication and emits CH4 to the atmosphere. So far, little is known about the rate of CH4 emission from lake aquaculture and about how ecological restoration (or aquaculture abandonment) affects the emission. In this study, the eddy covariance (EC) technique was deployed to quantify the CH4 flux in an enclosure lake aquaculture farm and to investigate the flux response to ecological restoration. The lake site under aquaculture farming emitted 36.0 g C-CH4 m(-2) yr(-1) to the atmosphere, an amount that is comparable to the global mean value of semi-intensive aquaculture systems. The annual CH4 emission decreased from the pre-restoration level by 34% and 37% to 23.7 and 22.8 g C-CH4 m(-2) yr(-1) in the first and second year of ecological restoration, respectively, but was still much higher than that at a reference lake site not impacted by aquaculture farming (6.12 g C-CH4 m(-2) yr(-1)). The high emission values after aquaculture abandonment suggest that aquafeed input in the decades of farming may have caused accumulation of a large amount of organic carbon in the sediment that continues to fuel CH4 production and that it may take a long time for the system to recover to a natural state. After aquaculture abandonment, floating-leaved plants expanded rapidly within the EC flux footprint, resulting in a high net ecosystem productivity (NEP) in the growing season. These plants appeared to be able to transport CH4 to the atmosphere through aerenchyma tissues and stomata.

Eddy covariance method has become a key technique to measure CH4 flux continuously in lakes. A large number of CH4 flux data was missing due to variable reasons. In order to reconstruct a complete time series of CH4 flux, it is necessary to find an appropriate gap-filling method to insert the CH4 flux data gap. Based on the routine meteorological data and CH4 flux data measured at Bifenggang site in the eastern part of the Taihu eddy flux network during 2014 to 2017, we analyzed the control factors of CH4 flux at the half-hour scale and daily scale. With those data, we tested that whether nonlinear regression method and two machine learning methods, random forest algorithm and error back propagation algorithm, could fill the CH4 flux gap at the half-hour scale and daily scale. The results showed that CH4 flux at the half-hour scale was mainly influenced by sediment temperature, friction velocity, air temperature, relative humidity, latent heat flux and water temperature at 20 cm in the growing season, and was mainly affected by relative humidity, latent heat flux, wind speed, sensible heat flux and sediment temperature in non-growing season. The CH4 flux at the daily scale was mainly affected by latent heat flux and relative humidity. Random forest model was the best in CH4 flux data gap filling at both time scales. The random forest model with the input variables of day of year, solar elevation angle, sediment temperature, friction velocity, air temperature, water temperature at 20 cm, relative humidity, air pressure, and wind speed was more suitable for filling the CH4 flux data gap at the half-hour scale. The random forest model with the input variables of day of year, sediment temperature, friction velocity, air temperature, water temperature at 20 cm, relative humidity, air pressure, wind speed, and downward shortwave radiation was more suitable for filling CH4 flux data gap at the day scale. The interpolation models could fill the data gap better at daily scale than that at the half-hour scale.

淡水养殖塘是甲烷(CH4)排放的热点区域.准确观测CH4年排放量还存在较大挑战,尤其是采用低频的观测方法.因此,本研究以亚热带长江三角洲区域典型淡水养殖塘为研究对象,基于涡度相关方法(Eddy covariance,EC)测定的2016—2020年养殖塘水-气界面高频连续CH4通量数据,探讨了对淡水养殖塘CH4通量进行箱式法等低频观测时,在一日内的最佳观测时间以及一年内的最佳观测日数,从而实现对CH4年排放量的准确估算.结果表明:一日当中最佳的观测时间春季为14:30—16:30、夏季和秋季为6:30—8:30、冬季为11:30—13:30,与EC连续观测获取的各季节日均值比较,以上选取方案估算的日均值不确定性最小,变化范围为0.1％～4％;在准确估算日均值的基础上,对于一年内的最佳观测天数,建议至少需要在全年均匀选取80 d,即观测频率为每月6～7 d,且均匀分布在每月的上中下三旬,才能够达到一年内连续观测获取的年均值±20％之内的高精度估算.当全年观测日数少于20 d时,CH4通量年均值估算的不确定性可高达50％.该研究结果可在无高频连续CH4通量观测前提下,为养殖水体CH4通量观测时段方案设计以及降低内陆水体碳收支估算不确定性等提供科学依据和参考.

本研究基于多通道密闭式动态箱法对亚热带典型养殖塘CH4通量的时空变化特征及其影响因素进行了分析.结果表明:亚热带养殖塘CH4主要排放方式是冒泡,CH4扩散及冒泡通量均呈现明显的季节变化特征.春、夏、秋、冬4个季节CH4扩散通量分别为:0.113,0.830,0.002,0.005μmol/(m2·s),冒泡通量分别为0.923,1.789,0.006,0.007μmol/(m2·s),冒泡通量占总通量的比例分别为89.04％、68.29％、78.95％和60.52％.在冬、春季养殖塘没有人工管理措施的情况下,CH4通量随着离岸距离的增加而增大,冬、春季养殖塘中间区域CH4总通量分别是岸边浅水区的34.70和2.98倍.夏季养殖活跃期CH4通量在空间上呈现出:人工投食区(7.371μmol/(m2·s))>自然生长区(2.151μmol/(m2·s))>人工增氧区(0.888μmol/(m2·s))>岸边浅水区(0.206μmol/(m2·s))的特征.在0.5h尺度上,春季CH4扩散通量与水温呈显著正相关关系,与风速呈负相关关系,秋季CH4扩散通量与水温、风速呈正相关关系,冒泡通量和水温呈正相关关系.在日尺度上,水温是CH4扩散通量和冒泡通量的主控因子,两者均随着水温升高呈指数增加,并且冒泡通量的水温敏感性Q10(12.72)大于扩散通量(7.78).

Aquaculture ponds are important anthropogenic methane (CH4) sources to the atmosphere. Currently large uncertainties still remain regarding the emission strength of this source type and its relationship with aquacultural farming practices. In this study, the methane flux was measured continuously for four years with eddy covariance (EC) in an aquaculture pond complex in the Yangtze River Delta, China. These ponds have never been dredged and were aerated during part of the aquacultural season. Additionally, floating chambers and inverted funnels were used to investigate spatial heterogeneity of the CH4 flux and to quantify the contribution via ebullition to the flux. The results showed that the daily CH4 flux ranged from 0.1 to 16.7 mu g m(-2) s(-1), with an average value of 4.10 +/- 3.08 mu g m(-2) s(-1). Water temperature was the primary driver of the CH4 flux across multiple time scales (half-hourly, daily, and monthly scale). Ebullition was the main transport way accounting for 70% +/- 4% of the total CH4 flux. The annual flux in this study was about three times the median flux reported by other researchers for similar freshwater aquaculture ponds. A statistical analysis of our data together with the published flux data reveals that ponds with dredging have much lower CH4 emission flux than those without dredging and suggests that dredging may have a much larger influence on the emission flux than aeration.

Eddy covariance data are widely used for the investigation of surface–air interactions. Although numerous datasets exist in public depositories for land ecosystems, few research groups have released eddy covariance data collected over lakes. In this paper, we describe a dataset from the Lake Taihu eddy flux network, a network consisting of seven lake sites and one land site. Lake Taihu is the third-largest freshwater lake (area of 2400 km2) in China, under the influence of subtropical climate. The dataset spans the period from June 2010 to December 2018. Data variables are saved as half-hourly averages and include micrometeorology (air temperature, humidity, wind speed, wind direction, rainfall, and water or soil temperature profile), the four components of surface radiation balance, friction velocity, and sensible and latent heat fluxes. Except for rainfall and wind direction, all other variables are gap-filled, with each data point marked by a quality flag. Several areas of research can potentially benefit from the publication of this dataset, including evaluation of mesoscale weather forecast models, development of lake–air flux parameterizations, investigation of climatic controls on lake evaporation, validation of remote-sensing surface data products and global synthesis on lake–air interactions. The dataset is publicly available at https://yncenter.sites.yale.edu/data-access (last access: 24 October 2020) and from the Harvard Dataverse (https://doi.org/10.7910/DVN/HEWCWM; Zhang et al., 2020).

冒泡是甲烷排放的主要途径之一,为量化太湖藻型湖区CH 4冒泡通量及其占总通量的比例,本研究采用静态箱—便携式温室气体自动分析仪方法对春、夏季太湖梅梁湾进行了多日连续观测.结果表明,太湖藻型湖区春、夏季CH4冒泡通量均存在白天高于夜间的日变化特征.春、夏季CH4冒泡通量分别为1.843、104.497nmol/(m2·s),占总通量的比例分别为31.2％和68.6％,即冒泡是夏季CH4排放的主要方式,而春季CH4排放则以扩散为主.在小时及日尺度上,CH4冒泡通量与温度(气温、表面水温和底泥温度)和气压显著相关,且随着温度升高、气压降低,CH4冒泡排放分别呈指数增加和线性增加趋势.本研究可为准确估算太湖流域CH4总排放量及明确我国湖泊对全球碳循环的贡献提供重要的基础数据.

In order to identify CH4 and CO2 emission flux characteristics and their impact factors in the algal lake zone of Lake Taihu, CH4 and CO2 fluxes were observed by the improved closed chamber method in Meiliang Bay in Lake Taihu. The relationships between CH4 and CO2 flux and meteorological factors were analyzed. The results showed that CH4 and CO2 fluxes had obvious diurnal variations. The CH4 flux in the daytime was higher than that in the nighttime in spring; however, the CH4 flux in the nighttime was higher than that in the daytime in summer. The CO2 uptake flux in the daytime was higher than that in the nighttime in spring and summer. The algae zone of Lake Taihu was a CH4 source for the atmosphere. The average CH4 flux was 4.047 nmol ·(m2 ·s)-1 and 40.779 nmol ·(m2 ·s)-1 in spring and summer, respectively. The zone was the CO2 sink for the atmosphere in spring and summer. The average CO2 flux was -0.160 μmol ·(m2 ·s)-1 and -0.033 μmol ·(m2 ·s)-1 in spring and summer, respectively. On an hourly scale, the CH4 emission flux was positively correlated with air temperature and water temperature (r=0.20, P<0.01 and r=0.34, P<0.01, respectively). When wind speed was lower than 6 m ·s-1, the CH4 flux was positively correlated with wind speed (r=0.71, P<0.01). The CO2 uptake flux had a significant positive correlation with air temperature and wind speed (r=0.14, P<0.01 and r=0.33, P<0.05, respectively). However, the CO2 uptake flux was negatively correlated with air pressure and solar radiation (r=-0.41, P<0.01 and r=-0.35, P<0.01, respectively). The CO2 efflux had a significant positive correlation with wind speed (r=0.40, P<0.05). The CO2 efflux was negatively correlated with solar radiation (r=-0.35, P<0.01). On a daily scale, the CH4 emission flux had a significant positive correlation with air temperature and water temperature (r=0.83, P<0.01 and r=0.78, P<0.01, respectively).